“Bridges are really hard,… and there are like 500 bridges in Pittsburgh.”

Of course, it is the infinite (or near infinite) context that we, humans can process and machines aren’t even close … But, one would think bridges would be easier – no distractions, well designed straight roads; of course with the current GPS accuracy, the car might think that it is in the water and start rolling out it’s fins !!

“You have a lot of infrastructure on the bridge above the level of the car that we as humans take into account, … But when you sense those things with a sensor that doesn’t have the domain knowledge that we do … you could imagine that the girders coming up from the side of the bridge and that kind of thing would be disturbing or possibly confusing.”

In fact Pittsburgh is called “The City of Bridges”, even though some have different interpretations (we will come to that discussion in a minute)

While we are on the subject, I do have a couple of books for the Uber Car to read ! It can even order them through it’s robotic friend Alexa ! or drive to wherever fine books are sold, on it’s own time – Uber might not pay for the impromptu solo drive.

In short, a SLAM system needs known points in addition to unknown points, to reason about & figure out it’s trajectory – bridges have less of known points it can rely on …

We can definitely employ Deep Learning ConvNets as well as traditional computer vision with a dash of contextualization is a good start … that is a topic for another time (sooner than later…). Probably an interesting opportunity for bridges.ai or openbridges.org

For those snappy Machine Learning experts, there is even a Pittsburgh Bridges Data Set at UCI, to start with ! Probably nowhere near the data needed to train modern Convolutional Nets, but one can augment the images with algorithms like Flip, Jitter, Random Crop and Scale et al.

If we think Pittsburgh is difficult, wait until Uber starts autonomous driving in Amsterdam ! While Pittsburgh has 446 bridges, many sources put Amsterdam with over 1000 bridges that cars can travel. There are many bicycle and pedestrian bridges in Amsterdam that an Uber car wouldn’t be interested in – except, of course, to pick up the tired pedestrians ;o). The which-city-has-max-number-of-bridges discussions can be followed here:

The books are more relevant now than then – you see, then it was science fiction, now the concepts are turning into reality !!!

As the Reddit Series Guide mentions, you can follow the publishing order or the internal story chronological order. But both are non-optimal and I think the orders would interfere with the reader’s thinking.

Isaac Asimov, himself, has suggested an order, which is more closer to my thinking but still not quite …

[Note : I pieced together the list from various discussions in Reddit and will note original comments within quotes]

First things first – read the Robot Series, in chronological/publication order. You have to meet Elijah Baley and R. Daneel Olivaw !

A) The Caves of Steel

B) The Naked Sun

C) The Robots of Dawn

D) Robots and Empire

Then comes the Foundation Series.

“The two common recommendations are to read these either publication order or chronological order.

I have a third recommendation: start with the original trilogy, then read the prequels, and end with Edge and Earth. …

This gives a good arrangement stylistically, with the earlier novels followed by the later ones. Asimov’s writing style changes distinctly over time. It also gives a good arrangement chronologically, with the prequels foreshadowing the final two books, instead of explaining things you’ve already read about.

And best of all, you end with the cliffhanger, instead of reading it and then reading 2-5 more books that don’t resolve it.”

The following order “preserves the mystery the first-time reader would have going into the first Foundation book. Part of the enjoyment of the Foundation novel is that you don’t know who Seldon is, in those opening scenes on Trantor, or what role he’s going to play in the story. If you read Prelude and Forward first, you’ll already have an earful about Trantor and Seldon before you get to Seldon’s introduction through Gaal Dornick’s eyes in Foundation”

Then you can diverge to other books like Nemesis and The End Of Eternity. The [Galactic] Empire Series are not essential, but do read them – “The Currents of Space”, “The Stars, Like Dust” and “Pebble in the Sky”. Publishing order is fine.

Now you are part of the Asimov club – And have one interesting task to do – which is feedback ! Add comments to this blog with insights – you could even add a new roadmap guid of your own with a very different POV !!!

Lesson 1 : Our machines inherit our faults (so far …)

Lesson 2 : Many domains are not forgiving to byzantine failures

We are learning that painful lesson whether they are rockets, airplanes or cars. Even though we freak out of snapchat is down for an hour, we can survive that, but not these. The drivers need to understand the downside of technologies and be alert.

For example multiple sensor sources & probably independent situational interpretation. I saw the following from somewhere where the Japanese Ministry talks about “correcting the wrong train of thoughts”:

Lesson 4 : Swarm Intelligence

Lesson 5 : This might lead to some level of Standardization & Legalization

Both are must read & discuss for the AI community. The abstracted 8 rules are :

This could beg the question, what exactly is an AI ? Let me make an attempt from an autonomous vehicle (cars, drones et al) perspective, which might not be complete or sufficient for other situations ….

What says thee ?

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I am spending this weekend with Yann LeCun (virtually, of course) studying the excellent video Lectures and slides at the College de France. A set of 8 lectures by Yann LeCun (BTW pronounced as LuCaan) and 6 guest lectures. The translator does an excellent job – especially as it involves technical terms and concepts !

Took the Norwegian flight OAK-GTW. This is a good alternative to London. Am also flying by Norwegian to Gothenberg.

The OAK-GTW fight was a 787-8 ! Good plane – I really like the window – a lot bigger … and it has the hi-tech ElectroChromatic Dimming System (or Sun Glasses as they are colloquially called ! ) that replaces the window shutter – you can always see the outside.

Exponential Advances:

An interesting article in Nature points out that exponential advanced in technological growth can result is a very alternate world very soon.

IBM X Prize:

And the IBM AI X Prize is offering a chance to showcase powerful ideas that tackle challenges.

Got me thinking … What do would we want our machines/AI to do ?

I am interested in your thoughts. Pl comment on what you would like AI to do.

Earlier I had written about us not wanting our machines to be like us; understand us – may be, help us – definitely, but imitate us – absolutely not …

So what does that mean ?

Driving cars ? – Definitely

Image recognition, translation and similar tasks ? – Absolutely

Write like Shakespeare just by feeding all the plays to a neural network like the LSTM ? – Definitely not !

I see folks training deep Learning systems by feeding them Shakespeare plays and see what the AI can write. Good exercise, but is that something we would get an X Prize for ? Of course, that is putting the cart before the horse !

We don’t write just by memorizing the dictionary and Elements of Style !!

We write because we have a story to tell.

The story comes before writing;

Experience & imagination comes before a story …

A good story requires both the narrative power as well as a powerful content with it’s own anti-climax, and of course the hanging chads ;o)

Which the current AI systems do not possess …

Already we have robots (Google Atlas) that can walk like a human – leaving aside the the goofy gait – which, of course, is mainly a mechanical/balance problem than an AI challenge

Robots can drive way better than a human

They translate a lot better than humans can (Of course language semantics is a lot more mechanical than storytelling)

Or is AI just a mechanical fallacy as Kasparov points out “… only intelligent the way your programmable alarm clock is intelligent“

In many ways, by helping AI to understand us, the ultimate utility might not be whether AI really comprehends us or not, but whether we get to understand us better, in the process !! And that might be the best outcome out of all of these innovations.

Over the past 100 years, we’ve been training humans to be as punctual and predictable as machines; … we’re so used to being machines at work—AI frees us up to be humans again ! – Well said SriSatish

With these points in mind, it is interesting to speculate what the AI X-Prize TED talks would look like in 2017; in 2018. And what better way to predict the future than to invent it ? I am planning on working on one or two submissions …

Good insights into what Cognitive Computing is, as a combination of Intelligence(Algorithms), Inference(Knowledge) and Interface (Visualization, Recommendation, Prediction,…)

IMHO, Cognitive Computing is more than Analytics over unstructured data, it also has touches of AI in there.

Reason being, Cognitive Computing understands humans – whether it is about buying patterns or the way different bodies reacts to drugs or the various forms of diseases or even the way humans work and interact

And that knowledge is the difference between Analytics and Cognitive Computing !

I like Cognitive Computing as an important part of AI, probably that is where most of the applications are … again understanding humans rather than being humans !